Applications and management of artificial intelligence for the health sector #254016

Upon completion of this course, participants will be able to:

• Understand the AI Landscape in Healthcare: Define key AI concepts and identify their practical applications across clinical, operational, and research domains.

• Evaluate AI Solutions for Clinical Use: Assess the validity, efficacy, and potential impact of AI-driven tools for diagnostics, treatment planning, and predictive analytics.

• Navigate the Regulatory and Ethical Framework: Understand the regulatory approval processes (e.g., FDA for SaMD), data privacy laws (HIPAA, GDPR), and ethical principles governing AI in healthcare.

• Develop an AI Implementation Strategy: Create a framework for the successful integration, workflow redesign, and change management required to adopt AI technologies.

• Manage Data for AI: Understand the data infrastructure, quality, and governance needed to fuel effective and unbiased AI systems.

• Calculate ROI and Make Informed Decisions: Evaluate the financial implications, total cost of ownership, and potential return on investment of AI projects in a healthcare setting.

This course is designed for a multidisciplinary audience involved in the delivery, management, and advancement of healthcare services:

• Healthcare Executives & Administrators: Hospital CEOs, CTOs, CIOs, COOs, and department heads responsible for strategic planning and technology investment.

• Clinical Leaders: Chief Medical Officers (CMOs), Chief Nursing Officers (CNOs), Medical Directors, and Department Chairs.

• Practicing Clinicians: Physicians, surgeons, radiologists, pathologists, and nurses interested in the application of AI in their clinical practice.

• Healthcare IT & Informatics Professionals: IT directors, clinical informaticists, data analysts, and health information managers.

• Policy Makers & Regulatory Affairs Specialists: Professionals involved in health technology assessment, policy, and regulation.

• Researchers & Academics: Those conducting medical research who want to leverage AI tools.

• Representatives from Pharma and MedTech: Professionals involved in developing or commercializing AI-powered health products.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of AI in Healthcare

• AM: The AI Revolution in Medicine

  • Demystifying AI, Machine Learning, and Deep Learning for a healthcare audience.

  • Overview of the current state: From hype to reality. Key success stories and lessons learned.

  • The potential impact on patient care, cost reduction, and accessibility.

• PM: The Healthcare Data Ecosystem

  • The fuel for AI: EHR data, medical imaging (DICOM), genomic data, wearables, and IoT.

  • Challenges of data interoperability, standardization, and quality.

  • Introduction to data governance and master data management in healthcare.

Day 2: Clinical Applications of AI

• AM: AI in Medical Imaging and Diagnostics

  • Deep dive into radiology, pathology, ophthalmology, and dermatology.

  • How AI assists in detection, characterization, and quantification of disease.

  • Case Study: Analyzing the impact of an AI-based stroke detection tool.

• PM: AI in Clinical Decision Support and Treatment

  • Predictive analytics for early warning systems (e.g., sepsis, patient deterioration).

  • Personalized medicine: AI in genomics and drug selection.

  • Robotic Process Automation (RPA) for administrative task reduction.

  • Workshop: Evaluating a CDS tool using a real-world clinical scenario.

Day 3: Operational and Administrative Applications

• AM: Streamlining Healthcare Operations

  • AI for hospital operations: Predictive patient admission rates, bed management, and staff scheduling.

  • Optimizing revenue cycle management: AI in claims processing and denial prevention.

  • Enhancing patient engagement: Chatbots for symptom checking and appointment scheduling.

• PM: AI in Drug Discovery and Public Health

  • Accelerating drug discovery and clinical trials through AI.

  • Population health management: Identifying at-risk cohorts and predicting health trends.

  • Group Discussion: Prioritizing AI use cases based on institutional strategic goals.

Day 4: Implementation, Strategy, and Management

• AM: The Path to Implementation

  • Developing a business case and calculating ROI for AI projects.

  • The procurement process: Evaluating vendors and technology partners.

  • Workflow integration and change management: Getting clinician buy-in.

• PM: Regulatory, Legal, and Ethical Considerations

  • Navigating FDA approval for Software as a Medical Device (SaMD).

  • Ensuring compliance with HIPAA and data security best practices.

  • Addressing algorithmic bias, ensuring fairness, and maintaining transparency (Explainable AI in medicine).

  • Understanding medical liability and the question of accountability.

Day 5: Leadership and the Future

• AM: Building an AI-Ready Organization

  • Developing an AI strategy and governance framework.

  • Talent strategy: Upskilling teams and defining new roles.

  • Fostering a culture of innovation and data-driven decision-making.

• PM: Capstone Project and Looking Ahead

  • Capstone Exercise: Teams develop a proposal for implementing a specific AI solution, covering clinical need, vendor selection, implementation plan, and evaluation metrics.

  • Future Trends: The rise of Generative AI in healthcare, ambient intelligence, and the future of the AI-augmented clinician.

  • Course Wrap-Up: Final presentations and creating a personal action plan.